Citation and License

BMC Infectious Diseases 2011, 11:331
doi:10.1186/1471-2334-11-331

Published: 2 December 2011

Abstract

Background

Hemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused
by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution,
external environmental factors including climate factors may play a significant role
in its transmission. The city of Shenyang is one of the most seriously endemic areas
for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified
climate-related risk factors and their roles in HFRS transmission in Shenyang, China.

Methods

The annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated
and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation
and autocorrelation analyses were performed to detect the lagged effect of climate
factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal
component analysis was constructed by using climate data from 2004 to 2009 to extract
principal components of climate factors to reduce co-linearity. The extracted principal
components and autocorrelation terms of monthly HFRS cases were added into a multiple
regression model called principal components regression model (PCR) to quantify the
relationship between climate factors, autocorrelation terms and transmission of HFRS.
The PCR model was compared to a general multiple regression model conducted only with
climate factors as independent variables.

Results

A distinctly declining temporal trend of annual HFRS incidence was identified. HFRS
cases were reported every month, and the two peak periods occurred in spring (March
to May) and winter (November to January), during which, nearly 75% of the HFRS cases
were reported. Three principal components were extracted with a cumulative contribution
rate of 86.06%. Component 1 represented MinRH0, MT1, RH1, and MWV1; component 2 represented RH2, MaxT3, and MAP3; and component 3 represented MaxT2, MAP2, and MWV2. The PCR model was composed of three principal components and two autocorrelation
terms. The association between HFRS epidemics and climate factors was better explained
in the PCR model (F = 446.452, P < 0.001, adjusted R2 = 0.75) than in the general multiple regression model (F = 223.670, P < 0.000, adjusted R2 = 0.51).

Conclusion

The temporal distribution of HFRS in Shenyang varied in different years with a distinctly
declining trend. The monthly trends of HFRS were significantly associated with local
temperature, relative humidity, precipitation, air pressure, and wind velocity of
the different previous months. The model conducted in this study will make HFRS surveillance
simpler and the control of HFRS more targeted in Shenyang.